AGRICULTURAL WASTE REUSES PLANNING: A CASE STUDY IN TAIWAN
Ya-Hsuan Chou1, Shu-Kuang Ning1 and Ho-Wen Chen2 1National University of Kaohsiung, Taiwan 2Chao-Yang University of Technology, Taiwan
The dependence on imported fossil-based energy is up to 99.34 % in Taiwan. To ensure national security and mitigate CO2 emission problem, Taiwan government regards bioenergy as one of the primary alternative energy, especially in agricultural-waste utilizations. However, restricted by dispersed distribution of cultivation land, the objective of this research is to establish an optimal biomass using policy, including the location and collecting route decision of bioenergy plant. Analytical Hierarchy Process, Geographical Information System and mathematical programming model have been used to obtain the optimal locations and their collection routes; finally, a case study has been accomplished in the research.
Keywords:
agricultural waste management, route planning, siting strategy, optimal analysis
INTRODUCTION
According to the lack of energy resources, Taiwan highly depends on imported energy; most of them are fossil fuels. With the increasing awareness of fossil fuel, Taiwan would like to develop local renewable energy to adapt the variation of fuel price and the climate change issue. Mohamed and Lee [1] mentioned that Malaysia government has adjusted their energy policy from four-fuel to five-fuel diversification strategy to ensure long-term reliability and security of energy supply. Due to the abundant of palm oil, the development of bioenergy is adopted as major project. As for India, Ravindranath and Balachandra [2] evaluated the potential of agricultural waste and energy crop throughout India, the cultivated area of rice is the biggest, 46.1Mha, the rice-associated waste would generate 4700 MW by gasification.
Taiwan is an island located in subtropical area, the rice is the major food crop of Taiwan; hence, rice-residue can be regarded as biomass to generate bioenergy. Hoogwijk et al. [3] analyzed the global potential of bioenergy, including the energy crop, agricultural and forest waste, the result showed that the cultivation of energy crop on the fallows make huge contribution to diverse the energy resource. In present, the common treatment of rice straw is on-site burning for producing manure in Taiwan; however, the open burning is harmful to air quality, such as visibility and SOx, NOx pollutants. In this regard, the reutilization of rice straw not only saves the cost of disposal, mitigates the damage of air quality, but also produces valuable bioenergy, helping achieving the goal of resources recovery and reuse. (Tu et al., 2009)[4]
Due to the dispersed distribution of cultivation land, an effective utilization of biomass is essential for Taiwan. In this regard, the research would like to establish a suitable siting strategy and optimize the collection route of bioenergy plant by AHP, GIS and optimization analysis tools.
METHODOLOGY
The research is about to develop series steps to find out an optimal site and collecting route the bioenergy plant. First of all, the criteria of choosing a suitable site for bioenergy plants are difficult to be identified. To compare the importance among the three aspects, including environment, economic and society, AHP method was used as a tool for deciding the weights of factors; following the GIS is used to determine the candidate sites by combining the spatial information and weights of factors. An optimal model is established at last to optimize the terminal location and collection route of bioenegy plant.
Candidate sites selection
AHP is a powerful yet simple way to weight selective factors in the research. The AHP first conduct a hierarchy as shown in figure 1, concerning the problem related aspects, including environmental, economical and social aspects. After the hierarchy is built, the weights between factors would be evaluated through the expert questionnaires. The weights obtained from AHP are consequently applied to determine the candidate sites by overlaying analysis tools of GIS.
GIS is an effective tools to deal with spatial information, the research adopted the overlay tools of GIS which was combined the weights gathered from AHP to obtain the location of candidate sites.
Optimization model
An optimal model of collecting routes and fallows reuse is established after the determination of candidate sites. The objective of the model is the minimization of collecting distance, and the limitation of capacity of plants, collecting time etc.
(1) where i, j are sources of biomass collection (vertex); k is order number of collection route; N is the set of all vertices (N*=1~N*, N*+1~N=N0, N=N0+N*); N0 is set of plants; N* is set of collection vertices; K is number of bioenergy plants; and Dij means the collection distance between edge (i,j); xijk is the decision variable determining whether the edge (i,j) collection route of plant k should be chosen. If xijk =1, collection route from vertex i to vertex j of the kth plant is selected, or otherwise.
The constraints considered the limit of plant capacity, number of plants, collecting time, car consistency and inner sub-tour restriction in the optimal model. Collection status all of the collection vertices should be collected once and all of the plants should be collected less than once.
j=1, 2, …, N* (2)
i=1, 2, …, N* (3)
j=1, 2, …, N0 (4)
i=1, 2, …, N0 (5)
Site number constraint
(6)
(7)
where Sdemand means upper limit of site number.
Site capacity limit
k=1,2,..,K (8)
where Qj is the agriculture waste production of vertex j; Bmin and Bmax mean lower and upper limit of plant capacity (48,000~60,000 ton/yr), respectively. Collection time limit
k=1,2,..,K
(9) where Tij is collection time between vertex (i,j); Tmax means upper limit of collection time (8hr)
Selection limit of destination
Car conservation k=1,2,..,K i=1,2,…, N (11) Sub-tour restriction k=1,2,..,K (12) Binary variables
{ }
0,1 xijk∈(13)
Finally, LINGO 11.0 is employed to solve the optimization model used in the research.∑∑∑
= = = K 1 k N 1 j N 1 i ijk ijx D Min 1 x N 1 i K 1 k ijk≤∑∑
= = 1 x N 1 j K 1 k ijk≤∑∑
= = 1 x N 1 i K 1 k ijk =∑∑
= = 1 x N 1 j K 1 k ijk =∑∑
= =∑∑
= = ≤ ≤ N 1 j max N 1 i ijk j min Qx B B∑∑
= = ≤ N 1 j max N 1 i ijk ijx T T demand K 1 k N 1 j * N 1 i ijk S x 0 ≤∑∑∑
= = = demand K 1 k * N 1 j N 1 i ijk S x 0 ≤∑∑∑
= = =∑
∑
= = = − N 1 j N 1 j jik ijk x 0 x N ..., 2, S 1 S x S j i, ijk ≤ − ⊆∑
∈Figure 1 Hierarchy structure
K 1,2,..., k X X X * 0 * N 1 i N 1 i N 1 j ijk N 1 i N 1 j ijk N 1 j ijk ≥ =
∑
∑
∑∑
∑∑
= = = = = = (10)CASE STUDY
The centrial Taiwan has abundant rice production among Taiwan counties, especially are Changhua and Yunlin Counties, they accounts for 37 % of the production of whole Taiwan in 2008 (Council of Agriculture, Executive Yuan, 2008) [5]; currently, the residue of rice can be further regarded as one of the alternative energy resources excepted the fossil fuel, solar energy and wind power, the utilization of rice residue contributes significantly to the diversity and localization of energy resources. Changhua and Yunlin and part of Chaiyi Counties were selected as the case study area. The study area is abundant of rice and the agricultural population density is higher. The region of research is shown in figure 1.
Candidate sites
The proposed methodology is applied to the rice straw collection of case study area in central Taiwan. The weights of each factor are evaluated by the expert questionnaires of AHP; table 1 shows the factors we considered in the research and their weights. Accordingly, the candidate sites could be selected by overlay tools in GIS with the weights acquired from the AHP results.
As shown in table 1, it is can be discovered that environmental impact aspect is generally accepted more attention among the three aspects, especially is air pollution impact. As for construction cost aspect, stock acquisition cost has higher weight, and the employment opportunity is more important within social impact aspect.
Figure 2 demonstrated the distribution of siting potential of bioenergy plants in the study area. According to the suitability of each cell in case study area, there are 14 candidate sites are chosen in this study. Most of them centralized on Yunlin and Chiayi County; it may caused by the lower sensitivity in air and noise pollution impact in the case study area.
Optimal location and collection route decision
Figure 3 shows the distribution of candidate sites and collection points. Following the collection routes programmed by the optimization model developed in this study are shown in figure 4, of which the highlighted points are the optimal locations of bioenergy plant.
Due to the dispersed distribution and lower production, collection distance of route 2 is much longer than others to meet the capacity requirement and transport the residue from farther collection points to candidate sites. Besides, we also can see that the fifth collection route is the shortest, it’s because of the cultivation density is higher than others in the region.
Figure 2 Study area
Figure3 candidate sites
Aspects Assessment factors Weights
Construction cost Land acquisition cost 0.055
Stock acquisition cost 0.124
Electricity/water acquisition cost 0.071
Environmental impact Air pollution impact 0.263
Noise pollution impact 0.135
Traffic volume impact 0.103
Social impact Population growth rate 0.071
Employment opportunities rate of change 0.107
Land price rate of change 0.072
Figure 3 Candidate sites and collection nodes
Table 2 Case study result
Route Waste (ton) Collection distance (m) Collection efficiency (ton/m) 1 52626.49 88574.9 0.59 2 49979.82 134756.07 0.37 3 56404.45 96578.46 0.58 4 51183.65 109729.15 0.47 5 59318.69 45733.91 1.3 6 59663.62 80531.99 0.74 total 329,238.1 555,904.48 0.59
In addition, the result shows that the optimization model indeed helping collecting system to find out the nearest sites to become the final bio-energy plant locations, and the collection route is also can be decided. Table 2 displays the collection status result of the case study, such as the amount of waste and the collection distance; the collection efficiency is further defined here as the amount of waste that can be collected in given distance.
According to table 2, it is not surprised to illustrate that the collection efficiency of route 2 is the lowest; in contrast, due to the abundant production (straw) on collection point 24~28, route 5 owns the highest collection efficiency; therefore, it can be assumed that the efficiency for developing a bioenergy pilot plant on site 5 is the highest.
CONCLUSION AND SUGGESTION
The scale of agriculture in Taiwan is relative
smaller than other countries. However, under the pressure of energy supply stability and the global warming issue, the utilization of agriculture waste to generate bioenergy are essential within following decades. In this regard, how to use biomass effectively becomes the most important task in Taiwan.
The optimization model described in this study can preliminary take the collection distance into account to program the siting strategy of bioenergy plant, and the collection route can also be determined simultaneously. According to the result, there are some conclusions summarized here:
1. An AHP hierarchy is established to assess the suitability of location of bio-energy plant, including the construction cost, environmental impact and social impact aspects.
2. The results obtained by AHP method show that the air pollution and noise pollution factors are the most critical factor while dealing with the siting strategy of bioenergy plant.
3. There are 14 candidate sites selected by AHP method, and most of them concentrate within two centralized area.
4. In consideration of collection distance, the model indeed draw up the location and collection route of bioenergy plant in the same time ; there are 6 optimal sites and routes obtained to collect the biomass efficiently.
However, there are still some advanced issue could be improved in the future:
1. Other kinds of biomass could be taken into account simultaneously to expend the production of bioenergy.
2. The construction cost could be considered to in accordance with real situation more completely. 3. The reactivation of fallow land can be analyzed to
increase the collection effieincy, and active the agriculture of Taiwan in the future.
References
[1] A. R. Mohamed and K. T. Lee, “Energy for sustainable development in Malaysia: Energy policy and alternative energy”, Energy Policy. 34, 2006, pp. 2388-2397.
[2] N. H. Ravindranath and P. Balachandra, “Sustainable bioenergy for India: Technical, economic and policy analysis”, Energy. 34, 2009, pp.1003-1013. [3] M. Hoogwijk et al., “Exploration of the ranges of the global potential of biomass for energy”, Biomass and
Bioenergy. 25, 2003, pp.119-133.
[4] W. K. Tu et al., “Products and bioenergy from the pyrolysis of rice straw via radio frequency plasma and its kinetics”, Bioresource Technology. 100, 2009, pp. 2052-2061.
[5] Executive Yuan, “Directorate-General of budget, accounting and statistics”, 2008.
Figure 4 Collection nodes
